Hierarchical diagnosis method for pathogenesis of lymphocytosis of phagocytic cells

Abstract

The invention belongs to the technical field of biomedical research, and particularly relates to a hierarchical diagnosis method for pathogenesis of hemophagocytic lymphocytosis, which comprises the following steps: S1, collecting peripheral blood samples of a plurality of secondary HLH patients, and carrying out pathogenesis clinical grouping on each secondary HLH patient; s2, carrying out mNGS detection on the peripheral blood sample, establishing an innovative analysis strategy and method based on mNGS data, and carrying out integrated analysis and key evaluation parameter screening on patient chromosome copy number variation (CNV) data and microorganism data generated by mNGS. A set of clinical evaluation model for performing layered diagnosis on complex etiology such as infection, tumor and autoimmunity on HLH patients is established, etiology accurate screening and auxiliary layered diagnosis can be performed on clinically diagnosed secondary HLH patients, the economic cost and time cost of disease diagnosis are greatly reduced, and clinical accurate diagnosis and treatment and patient benefit are facilitated.

Description

Technical Field

[0001] The invention relates to the field of biomedical research, and in particular to a method for diagnosing the etiology of hemophagocytic lymphohistiocytosis. Background Art

[0002] Hemophagocytic lymphohistiocytosis (HLH), also known as hemophagocytic syndrome, is a life-threatening hyperinflammatory syndrome caused by abnormal activation of cytotoxic T lymphocytes, natural killer cells, and macrophages, resulting in hypercytemia and immune-mediated damage to multiple organs. As a syndrome, the clinical manifestations of HLH vary, including fever, organomegaly, pancytopenia, consumptive coagulopathy, hypertriglyceridemia, and elevated acute phase reactants. Subspecialists in different fields such as hematology / oncology, infectious diseases, and rheumatology / clinical immunology are challenged by this rare, multifaceted syndrome, as patients often present with recurrent fever, pancytopenia, abnormal liver function, and sepsis-like syndromes, and may rapidly develop end-stage multi-organ failure. Therefore, despite being a rare disease, clinicians should be aware of HLH, as early recognition can avoid irreversible organ damage and subsequent death.

[0003] HLH is usually divided into primary (inherited) and secondary (reactive), with primary HLH being the main subtype in pediatric patients and secondary HLH being the most common type in adult patients. Secondary HLH is usually triggered by infection or malignancy, but can also be induced by autoimmune diseases and drugs. Although HLH can occur at any age, most clinical guidelines, prospective studies, and treatment trials focus on pediatric patients. The HLH-94 and HLH-2004 treatment protocols have been established as the scientific cornerstone for the diagnosis, classification, and treatment of HLH patients under the age of 18. In 2019, the HLH Steering Committee of the Histiocyte Society developed recommendations for the diagnosis and treatment of HLH in adults. Infection is the most common cause of HLH, and a variety of infectious pathogens are associated with HLH, mainly viruses such as Epstein-Barr virus (EBV) and cytomegalovirus (CMV), but also bacteria, parasites, and fungi. Viral infection is reported to be the most common triggering factor, either as a primary infection in healthy individuals or as reactivation in immunosuppressed patients, with herpes viruses accounting for 62% of viral infection cases in adults with HLH, 43% of viral infection cases caused by EBV, and 9% of viral infection cases caused by CMV. Bacterial infection has been reported in 9% of adult HLH cases, of which 38% are caused by tuberculosis. Parasites and fungi are rare triggers of HLH, with Histoplasma capsulatum, Leishmania, Plasmodium, and Toxoplasma being the most commonly reported pathogens. Although there are many reported infections associated with HLH, some cases are affected by geographic region (leishmaniasis and tick-borne diseases), season (influenza virus, tick-borne diseases), and socioeconomic status (tuberculosis). HLH in adults is also associated with a variety of malignancies, including T-cell or natural killer cell lymphomas, B-cell lymphomas, leukemias, Hodgkin lymphomas, other hematological tumors, solid tumors, and other nonspecific tumors. In addition, HLH may also occur during chemotherapy and is often associated with infection.

[0004] Because the etiology of HLH is complex, patients suspected of HLH need to be fully evaluated and systematically diagnosed. In clinical practice, once a patient is suspected of having HLH, multiple evaluations should be initiated simultaneously. Laboratory and imaging tests should be performed to collect supportive evidence for the diagnosis of the HLH syndrome and to assess which organ systems are affected and the severity of the involvement. All patients should also be evaluated for infection and malignancy, including laboratory tests, bone marrow evaluation, systemic imaging, and biopsy of any suspicious results. Because HLH is an aggressive and fatal syndrome, early diagnosis and etiology identification are essential to initiate appropriate treatment and improve the patient's quality of life and survival rate. However, it is challenging to quickly determine the potential infectious etiology of HLH because traditional etiology diagnosis is time-consuming and sometimes the pathogen cannot be identified. In addition, traditional malignancy diagnosis is also time-consuming, has a low positive rate, and greatly increases the psychological and economic burden on patients due to invasive measures. The emerging metagenomic next-generation sequencing (mNGS) may be a potential optimal solution to help improve the clinical diagnosis of potential infections in hematological diseases. Due to its unbiased, rapid and extensive detection capabilities, mNGS has been applied to many complex infection cases associated with HLH and has successfully identified potential pathogenic microorganisms. However, traditional mNGS can only be used to quickly identify pathogens associated with infectious diseases, or only to simultaneously qualitatively assess the possibility of tumors when detecting infectious pathogens. However, whether the microbial data generated by mNGS and the patient's genomic information can be used to perform etiological screening and complex etiological stratified diagnosis of HLH patients, there is no relevant theoretical and clinical application research, which is a problem to be solved in the field, so the present invention provides a method for etiological stratified diagnosis of hemophagocytic lymphohistiocytosis. Summary of the invention

[0005] The object of the present invention is to provide a method for diagnosing the etiology of hemophagocytic lymphohistiocytosis in order to solve the above problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a method for diagnosing the etiology of hemophagocytic lymphohistiocytosis, comprising the following steps:

[0007] S1: Peripheral blood samples were collected from multiple patients with secondary HLH, and each secondary HLH patient was divided into clinical groups according to etiology;

[0008] S2: Perform mNGS testing on peripheral blood samples and use the data to conduct subsequent identification of microorganisms and infectious pathogens, as well as chromosome copy number variation analysis based on human genome sequences;

[0009] S3: DNA extraction and sequencing library preparation were performed on peripheral blood samples. Before sequencing, the quality of DNA was assessed using an analyzer combined with quantitative PCR to measure the adapter, the concentration of the library was adjusted, and NGS sequencing was performed using a single-end (SE) 50 base pair (bp) sequencing strategy on the sequencer;

[0010] S4: Obtain sequencing data from samples and determine the microbial species present in each sample by comparing with the database;

[0011] S5: All microorganisms detected in clinical samples were compared and screened with those detected in parallel NTC (no template control) (background microorganisms) according to the parameter of unique reads per million (RPM), among which microorganisms not detected in parallel NTC, and RPM (clinical samples): microorganisms with RPM (parallel NTC)>10 were defined as the real microbial flora in clinical samples, and all microbial species of the real microbial flora in clinical samples were searched in the literature, and microorganisms reported in the literature to cause infection in humans or mammals were defined as infectious pathogens;

[0012] S6: Abnormal CNV feature analysis: Sequencing reads were mapped to the human reference genome (hg19) in the NCBI database, and only uniquely aligned sequencing reads were selected for subsequent analysis. The reference genome was divided into continuous windows of fixed length, and the reading depth of each window was calculated and then normalized to the total number of reads for each sample. The normalized reading depth was divided by the average reading depth of the reference data set to obtain the copy number ratio of each window. The copy number was log2, and adjacent windows with similar ratios were merged into fragments, and the chromosome position and average ratio were annotated. The copy number of each fragment was calculated based on the average ratio and the normal copy number of the corresponding chromosome, and then compared with the preset threshold to verify the CNV;

[0013] S7: Statistical analysis: All collected data were statistically analyzed using the R software package. Categorical variables were expressed as frequencies and percentages and compared using Fisher's exact test. Normally distributed continuous measurement data were expressed as standard deviation of the mean or standard error of the mean. Non-normally distributed data were expressed as median (range). Differences and significance between groups were calculated using Student's t-test and Wilcoxon rank sum test or Kruskal-Walli test (non-normally distributed data). Data visualization was performed in R. Alpha diversity indices included Shannon index, richness index, Simpson index and inverse Simpson index. Student's t-test was used to evaluate differences between groups. Beta diversity was described using unconstrained PCoA (principal coordinate analysis). Similarity analysis (Anosim) was used to test differences between groups. Linear discriminant analysis effect size (LEfSe) analysis was performed to identify microorganisms with significant abundance differences between groups, with a threshold of log10. The LDA (linear discriminant analysis) score was ≥ 2 and the P value was ≤ 0.05. Before analyzing the relative abundance of microorganisms, the RPM values ​​of microorganisms were logarithmically transformed. The random forest method was used to build the prediction model. The number of important variables for classification was determined by the ten-fold cross-validation method. The prediction performance of the classifier was analyzed by the ROC curve.

[0014] Preferably, in step S1, the etiology of the clinical grouping includes HLH caused by EBV, HLH caused by malignant tumors, HLH caused by rheumatic diseases and HLH caused by other pathogens.

[0015] Preferably, in step S3, the analyzer is BioAnalyzer 2100 and the sequencer is Illumina Next Seq 550 Dx system.

[0016] Preferably, in step S3, the concentration of the library is adjusted to target 20 million (M) reads.

[0017] Preferably, in step S4, the samples include clinical samples or parallel NTCs, and the clean sequences (reads) obtained after filtering the raw data are first compared with the NCBI database to determine the microbial species present in each sample.

[0018] Preferably, in step S5, a literature search is performed on PubMed.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] An innovative analysis strategy and method based on mNGS data has been established. Through the integrated analysis of the patient chromosome copy number variation (CNV) data and microbial data generated by mNGS and the screening of key evaluation parameters, a clinical evaluation model for stratified diagnosis of complex etiologies such as infection, tumor and autoimmunity in HLH patients has been established. It can perform accurate etiological screening and auxiliary stratified diagnosis for clinically confirmed secondary HLH patients, and significantly reduce the economic and time costs of disease diagnosis, which is conducive to clinical precision diagnosis and treatment and patient benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The present invention relates to clinical diagnostic classification of clinical cases.

[0022] Figure 2 This is the primary etiology stratification of secondary HLH diagnosis based on CNV data analysis in Onco-mNGS in the present invention; wherein:

[0023] (A) The positive rate of copy number variation (CNV) detected by mNGS;

[0024] (B) Distribution and frequency of DNA segment gain (AMP, amplification) or loss (DEL, deletion) in EBV-HLH and M-HLH subtypes;

[0025] (C) Efficacy evaluation of the primary etiology stratification diagnostic model for secondary HLH based on CNV data (ROC curve);

[0026] (D) Schematic diagram of the first level of etiology for diagnosing secondary HLH.

[0027] Figure 3 is the distribution of pathogens identified by mNGS in the present invention; wherein:

[0028] (A) Heat map of pathogens identified by mNGS sequencing. The RPM of selected microorganisms was Log10 transformed. Samples were hierarchically clustered within each subtype using Pearson correlation as the distance metric for average linkage.

[0029] (B) Composition of pathogenic microorganisms in each subtype. All pathogens were determined by clinicians. Log-2-transformed RPM was used for analysis.

[0030] Figure 4 The pathogen composition in each sample collected from patients with EBV-HLH (A), non-EBV-HLH (B), M-HLH (C) and Rh-HLH (D) subtypes in the present invention.

[0031] Figure 5is the positive rate and diagnostic performance of mNGS in the present invention; wherein:

[0032] (A) Positive detection rate of mNGS in different HLH subtypes.

[0033] (B) Taxonomic composition of microorganisms identified by mNGS in all blood samples.

[0034] (C) Diagnostic performance of mNGS in identifying EBV-HLH subtypes (n=51) and nEBV-HLH subtypes (n=21) compared with the clinical gold standard. For the identification of EBV-HLH and nEBV-HLH subtypes, the sensitivity of mNGS was 86.7% and 100%, respectively.

[0035] Figure 6 is the detection rate and load of EBV and CMV in the blood of patients with different HLH subtypes in the present invention; wherein:

[0036] (A) EBV and CMV positivity rates detected by mNGS sequencing.

[0037] (B) EBV and CMV burdens among different HLH subtypes, and differences among groups were analyzed using T test.

[0038] Figure 7 In the present invention, based on the analysis of microbiome data from Onco-mNGS and clinical examination data, secondary etiology stratification diagnosis of secondary HLH is performed; wherein:

[0039] (A) Upset analysis plot of blood microorganisms with frequencies exceeding 3% in each HLH subtype determined by mNGS.

[0040] (B) Blood microbial burden in different HLH subtypes. Differences between groups were assessed by T test.

[0041] (C) Specific blood microbial biomarkers in different subtypes were determined by LEfSe analysis. The microbial species enriched in EBV-HLH, M-HLH, and Rh-HLH subtypes are shown in the figure with their respective mean RPM values ​​and LDA scores. The alpha value of the inter-class factorial Kruskal-Wallis test was 0.01, and the alpha value of the Wilcoxon test between pairwise subclasses was 0.05.

[0042] (D) Efficacy evaluation (ROC curve) of the etiology-stratified secondary diagnostic model for group 1 (CNV-positive EBVHLH and M-HLH) based on microbiome and clinical examination data.

[0043] (E) Efficacy evaluation of the etiology-stratified diagnostic model Efficacy evaluation of the secondary diagnostic model for etiology-stratification of group 2 (CNV-negative EBV-HLH, nEBV-HLH, and Rh-HLH) based on microbiome and clinical examination data (ROC curve).

[0044] (F) Stratification diagram of primary and secondary etiologies for the diagnosis of secondary HLH.

[0045] Figure 8 The present invention analyzes the composition and diversity of the blood microbiome of HLH patients with different etiologies; wherein:

[0046] (A) Heatmap of blood microorganisms with frequencies greater than 2% in each HLH subtype determined by mNGS. The RPM of selected microorganisms was Log10 transformed. Samples were hierarchically clustered within each subtype using Pearson correlation as the distance metric for average linkage.

[0047] (B) The microbial alpha diversity based on species profile within each subtype was assessed using the Shannon index, richness, Simpson index, and inverse Simpson index. Differences between groups were assessed using the anova method.

[0048] (C) Microbial beta diversity based on species characteristics among subtypes was assessed by unconstrained PCoA (principal coordinates PCoA1 and PCoA2) and permutational multivariate analysis of variance (PERMANOVA) by Adonis using Bray-Curtis distance.

[0049] Fig. 9 It is a flow chart of the steps of the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] In the description of the present invention, it should be noted that the terms "upper", "lower", "inner", "outer", "top / bottom" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific position, be constructed and operated in a specific position, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance.

[0052] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "provided with", "mounted / connected", "connected", etc. should be understood in a broad sense. For example, "connected" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0053] Example:

[0054] See also Figure 1-8 The present invention provides a technical solution: a method for diagnosing the etiology of hemophagocytic lymphohistiocytosis, comprising the following steps:

[0055] Random forest analysis of CNV signatures was performed to distinguish EBV-HLH and M-HLH from other HLH subtypes:

[0056] Human DNA sequences extracted from mNGS were used for chromosome copy number analysis. Figure 2 A analyzed the positive rate of CNV occurrence in each subtype. There are two subtypes with the most unstable genomes, namely EBV-HLH (43.14%) and M-HLH (36.36%) subtypes. CNV amplification and deletion were observed in different HLH subtypes. In EBV-HLH subtypes and M-HLH, the distribution of CNV on different chromosomes is different ( Figure 2B). The M-HLH subtype had a relatively high incidence of CNVs on all chromosomes except chromosome 21, while in the EBV-HLH subtype, CNVs on chromosome 19 were the most common genomic events. The types of CNVs on certain chromosomes also differed between the M-HLH subtype and the EBV-HLH subtype. On chromosomes 8 and 13, only CNV deletions occurred in the M-HLH subtype, while both CNV deletions and duplications occurred in the EBV-HLH subtype. The opposite results were observed on chromosomes 16, 18, and 19. In addition, the frequency of CNV deletions on chromosome 19 exceeded 40% in the EBV-HLH subtype, while it was only 25% in the M-HLH subtype. According to the CNV parameters, a random forest model was constructed and divided into two groups, including group 1 (partial subtype EBV-HLH and subtype M-HLH) and group 2 (partial subtype EBV-HLH, subtype nEBV-HLH, and subtype Rh-HLH), as the primary etiological stratification diagnosis of secondary HLH. The ratio of training set to test set was 3:1, and the ten-fold cross validation method was used to determine the number of important variables for classification. Figure 3 As shown in C, we evaluated the diagnostic performance of the classifier by ROC curve. The current classifier showed satisfactory diagnostic performance in typing EBV-HLH plus M-HLH subtype and nEBV-HLH plus Rh-HLH subtype, with an average AUC of 0.768. In summary, we drew a hierarchical diagnosis diagram to illustrate that the use of CNV-based random forest binary classifier model can better distinguish between group 1 and group 2 ( Figure 2 D).

[0057] Pathogen characteristics found by Onco-mNGS in patients diagnosed with different HLH subtypes:

[0058] A total of 18 pathogens were detected in all blood samples, including 11 viruses, 4 bacteria, and 3 fungi ( Figure 3 A). Human gammaherpesvirus 4 and human betaherpesvirus 5 were the most common viruses in all samples. In addition, Staphylococcus aureus was also the most common bacterial species. Based on Onco-mNGS sequencing, most samples had at least one pathogen identified. Among the EBV subtypes, the pathogen burden in each sample was relatively high. Figure 3The stacked bar chart in B shows the relative abundance of all pathogens in different subtypes, as well as the main pathogen species in each subtype. Human gammaherpesvirus 4 is the most abundant pathogen in the EBV-HLH subtype and is almost undetectable in the nEBV-HLH and Rh-HLH subtypes. Human betaherpesvirus 5 is commonly found in the three HLH subtypes other than the EBV-HLH subtype. In addition to the dominance of human betaherpesvirus 5, human alphaherpesvirus 1 and Torque teno virus are also common in the nEBV-HLH subtype, human gammaherpesvirus 4 and human betaherpesvirus 6B are also the main pathogens in the M-HLH subtype, and human alphaherpesvirus 3 is also common in the Rh-HLH subtype. The pathogen composition in samples collected from patients with different HLH subtypes is detailed in Figure 4 AD. Among the four HLH subtypes, the two subtypes caused by pathogen infection, namely EBV-HLH and nEBV-HLH, had a relatively high mNGS sequencing positivity rate, which is consistent with the clinical consensus ( Figure 5 A). 144 microorganisms were identified in HLH patient blood samples by mNGS sequencing, of which bacteria accounted for 79%, fungi accounted for 11%, and viruses accounted for 10% ( Figure 5 B). However, for further analysis, clinicians were also involved in determining the species identified by mNGS sequencing, with the goal of distinguishing pathogens from non-pathogenic microorganisms. In addition, the diagnostic performance of Onco-mNGS in identifying pathogens in patients with unknown etiology was evaluated. Compared with the gold standard method, the sensitivity of mNGS in distinguishing EBV-HLH and nEBV-HLH subtypes was 86.7% and 100%, respectively ( Figure 5 C). Figure 6 AB further illustrates the positive rate and load of two common pathogens (human gamma herpesvirus 4 and human beta herpesvirus 5) in the blood of patients with different HLH subtypes. In the EBV-HLH subtype, the positive rate of human gamma herpesvirus 4 was as high as 88.24%, and the pathogen load was significantly higher than that of other subtypes (P<0.001). Human beta herpesvirus 5 had the highest positive rate in the nEBV-HLH subtype, but there was no significant difference in the pathogen load of each subtype (P>0.05).

[0059] Etiology stratification of secondary HLH based on onco-mNGS-derived microbiological data and patient clinical data:

[0060] Microbial ecology is increasingly being implicated in disease development and progression, and thus the diversity and dynamics of the microbiome of each HLH subtype suggest distinct etiological mechanisms. The nEBV-HLH subtype had the most complex and diverse microbiome, with the most microbes identified, including 32 species that were uniquely present ( Figure 7A). We counted all microorganisms in each sample and summarized the corresponding RPM values ​​detected by mNGS, verifying the complexity of HLH subtypes as a whole ( Figure 7 B). The number of microorganisms in the two infection-induced subtypes, EBV-HLH and nEBV-HLH, was significantly increased compared with the Rh-HLH subtype (P < 0.05), but there was no significant difference compared with the M-HLH subtype. In addition, the total RPM value of all microorganisms in each sample was significantly increased in EBV-HLH compared with the other subtypes (P < 0.01). The overall situation of the four HLH subtypes is summarized by heat maps, see Figure 8 A. To compare the overall composition of blood microbial signatures of different HLH subtypes, the diversity within and between each subtype was analyzed using α-diversity and β-diversity ( Figure 8 B, 8C). The results showed that nEBV-HLH and M-HLH subtypes showed higher diversity after analysis of variance (P<0.05). Unconstrained PCoA (principal coordinates PCoA1 and PCoA2) and Bray-Curtis distance showed that the microorganisms detected in the EBV-HLH subtype were separated from other HLH subtypes on the first axis (P<0.05), indicating that the EBV-HLH subtype has a unique microbial community composition. In order to further explore the differences in microbial species between different HLH subtypes, the linear discriminant analysis effect size (LEfSe) method was used to determine the microorganisms that were significantly enriched in each subtype. A total of 21 microorganisms were screened for differential enrichment in HLH subtypes, most of which were significantly enriched in the M-HLH subtype ( Figure 7 C). Among these differential species, several species such as oral Streptococcus, Roseburia gingivalis, Pseudomonas, human alphaherpesvirus 3, human betaherpesvirus 5, and Bacillus cereus were enriched in the Rh-HLH subtype, but only human gammaherpesvirus 4 was significantly enriched in the EBV-HLH subtype. The average turnover number of human betaherpesvirus 5 in Rh-HLH was lower than that of the other three subtypes, so it was classified as a depleted microorganism. These different microorganisms provide evidence for the unique microbial community composition of different HLH subtypes.

[0061] After the above microbiome analysis, characteristic microbial information of different subtypes was obtained. Next, a random forest classifier model was constructed using characteristic microbial information and clinical examination data to distinguish between the CNV-positive EBV-HLH subgroup and M-HLH subgroup in the first group, and the CNV-negative EBV-HLH subgroup, nEBV-HLH subgroup, and Rh-HLH subgroup. The ratio of the training set to the test set was 3:1, and the ten-fold cross-validation method was used to determine the number of important variables for classification. Figure 7As shown in D, the diagnostic performance of the binary classifier was determined by the ROC curve, and it was found that the binary classifier could effectively distinguish the CNV-positive EBV-HLH subgroup and the M-HLH subgroup in group 1, with an AUC value of 0.833. At the same time, the random forest multivariate classifier constructed based on characteristic microbial information and clinical examination data could effectively distinguish the CNV-negative EBV-HLH subgroup, nEBV-HLH subgroup, and Rh-HLH subgroup in group 2, with AUC values ​​of 0.727, 0.718, and 0.656, respectively ( Figure 7 E). Finally, we drew a schematic diagram of the hierarchical diagnosis of secondary HLH, which included the first and second layers, and further explained that on the basis of the first-layer hierarchical diagnosis based on CNV, we can continue to use biomarker sets such as characteristic microbial information and preventive data of different subgroups to construct a random forest binary classifier or multi-classifier model for the second-layer etiology hierarchical diagnosis ( Figure 7 F).

[0062] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention; therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is limited by the attached claims rather than the above description. Therefore, it is intended to include all changes within the meaning and scope of the equivalent elements of the claims in the present invention, and any figure marks in the claims should not be regarded as limiting the claims involved.

[0063] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for diagnosing the etiology of hemophagocytic lymphohistiocytosis, characterized by: The following steps are involved: S1: Peripheral blood samples were collected from multiple patients with secondary HLH, and each secondary HLH patient was divided into clinical groups according to etiology; S2: Perform mNGS testing on peripheral blood samples; S3: DNA extraction and sequencing library preparation were performed on peripheral blood samples. Before sequencing, the quality of DNA was assessed using an analyzer combined with quantitative PCR to measure the adapter, the concentration of the library was adjusted, and NGS sequencing was performed using a single-end (SE) 50 base pair (bp) sequencing strategy on the sequencer; S4: Obtain sequencing data from samples and determine the microbial species present in each sample by comparing with the database; S5: All microorganisms detected in clinical samples were compared and screened with those detected in parallel NTC according to the parameter of unique reads per million (RPM), among which microorganisms not detected in parallel NTC and RPM (clinical samples): microorganisms with RPM (parallel NTC)>10 were defined as the real microbiome in clinical samples. All microbial species of the real microbiome in clinical samples were searched in the literature, and microorganisms reported in the literature to cause infection in humans or mammals were defined as infectious pathogens; S6: Abnormal CNV feature analysis: Sequencing reads were mapped to the human reference genome in the NCBI database, and only uniquely aligned sequencing reads were selected for subsequent analysis. The reference genome was divided into continuous windows of fixed length, and the reading depth of each window was calculated and then normalized to the total number of reads for each sample. The normalized reading depth was divided by the average reading depth of the reference data set to obtain the copy number ratio of each window. The copy number was log2, and adjacent windows with similar ratios were merged into fragments, and the chromosome position and average ratio were annotated. The copy number of each fragment was calculated based on the average ratio and the normal copy number of the corresponding chromosome, and then compared with the preset threshold to verify the CNV; S7: Statistical analysis: All collected data were statistically analyzed using the R software package. Categorical variables were expressed as frequencies and percentages and compared using Fisher's exact test. Normally distributed continuous measurement data were expressed as standard deviation of the mean or standard error of the mean. Non-normally distributed data were expressed as median (range). Differences and significance between groups were calculated using Student's t-test and Wilcoxon rank sum test or Kruskal-Walli test (non-normally distributed data). Data visualization was performed in R. Alpha diversity indices included Shannon index, richness index, Simpson index and inverse Simpson index. Student's t-test was used to evaluate differences between groups. Beta diversity was described using unconstrained PCoA (principal coordinate analysis). Similarity analysis (Anosim) was used to test differences between groups. Linear discriminant analysis effect size (LEfSe) analysis was performed to identify microorganisms with significant abundance differences between groups, with a threshold of log10. The LDA (linear discriminant analysis) score was ≥ 2 and the P value was ≤ 0.

05. Before analyzing the relative abundance of microorganisms, the RPM values ​​of microorganisms were logarithmically transformed. The random forest method was used to build the prediction model. The number of important variables for classification was determined by the ten-fold cross-validation method. The prediction performance of the classifier was analyzed by the ROC curve.

2. A method for stratified diagnosis of the etiology of hemophagocytic lymphohistiocytosis according to claim 1, characterized in that: In step S1, the etiology of the clinical grouping includes HLH caused by EBV, HLH caused by malignant tumors, HLH caused by rheumatic diseases and HLH caused by other pathogens.

3. A method for diagnosing the etiology of hemophagocytic lymphohistiocytosis according to claim 1, characterized in that: In step S3, the analyzer is BioAnalyzer 2100 and the sequencer is Illumina Next Seq 550 Dx system.

4. A method for diagnosing the etiology of hemophagocytic lymphohistiocytosis according to claim 1, characterized in that: In step S3, the concentration of the library is adjusted to target 20 million (M) reads.

5. A method for diagnosing the etiology of hemophagocytic lymphohistiocytosis according to claim 1, characterized in that: In step S4, the samples include clinical samples or parallel NTCs, and the clean sequences (reads) obtained after filtering the raw data are first compared with the NCBI database to determine the microbial species present in each sample.

6. A method for diagnosing the etiology of hemophagocytic lymphohistiocytosis according to claim 1, characterized in that: In step S5, a literature search is performed on PubMed.

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